Case Studies Designing a real-time data platform for the “Internet of Energy”

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	Designing a real-time data platform for the “Internet of Energy”

Designing a real-time data platform for the “Internet of Energy”

Technology Category	Analytics & Modeling - Real Time Analytics Application Infrastructure & Middleware - Data Exchange & Integration Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries	Utilities
Applicable Functions	Facility Management Quality Assurance
Use Cases	Energy Management System Machine Condition Monitoring Predictive Maintenance Real-Time Location System (RTLS)
Services	Software Design & Engineering Services System Integration
Challenge	Future Grid faced the challenge of processing extreme volumes of data in real-time for Australian utility companies. Traditional relational databases were inadequate for handling the 3 billion data points collected daily, leading to inefficiencies and high costs. The need for a real-time data aggregation solution was critical to enable complex, real-time decisions and overcome issues related to data volume, speed, reliability, resilience, and license costs. Read More
About Customer	Future Grid is a company that empowers data-rich enterprises to gain insights and control their future through its operational intelligence real-time data platform, the Future Grid Platform (FGP). The platform analyzes hundreds of millions of data points in real-time, enabling actions that reduce operational costs and increase revenue. It provides continuous, real-time complex event processing, real-time data aggregations, rich modeling tools, and contextual awareness. Future Grid works with several Australian utility companies to automate the processing of sensor and smart meter data across energy networks, handling approximately 3 billion data points daily. Read More
Solution	Future Grid built its platform using Hazelcast IMDG, an in-memory data grid designed for high availability and scalability. Hazelcast IMDG helps manage data and distribute processing using in-memory storage and parallel execution, providing significant speed and scale improvements. Future Grid integrated Hazelcast IMDG with Apache Cassandra, a distributed database for managing large amounts of structured data. This combination allowed Future Grid to overcome the limitations of traditional databases, providing continuous availability, linear scale performance, and operational simplicity. The integration enabled Future Grid to deliver real-time data processing and visualization, solving many challenges faced by the energy sector. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Real Time Analytics

Application Infrastructure & Middleware - Data Exchange & Integration

Platform as a Service (PaaS) - Data Management Platforms

Applicable Industries

Utilities

Applicable Functions

Facility Management

Quality Assurance

Use Cases

Energy Management System

Machine Condition Monitoring

Predictive Maintenance

Real-Time Location System (RTLS)

Services

Software Design & Engineering Services

System Integration

Challenge

Future Grid faced the challenge of processing extreme volumes of data in real-time for Australian utility companies. Traditional relational databases were inadequate for handling the 3 billion data points collected daily, leading to inefficiencies and high costs. The need for a real-time data aggregation solution was critical to enable complex, real-time decisions and overcome issues related to data volume, speed, reliability, resilience, and license costs.

About Customer

Future Grid is a company that empowers data-rich enterprises to gain insights and control their future through its operational intelligence real-time data platform, the Future Grid Platform (FGP). The platform analyzes hundreds of millions of data points in real-time, enabling actions that reduce operational costs and increase revenue. It provides continuous, real-time complex event processing, real-time data aggregations, rich modeling tools, and contextual awareness. Future Grid works with several Australian utility companies to automate the processing of sensor and smart meter data across energy networks, handling approximately 3 billion data points daily.

Solution

Future Grid built its platform using Hazelcast IMDG, an in-memory data grid designed for high availability and scalability. Hazelcast IMDG helps manage data and distribute processing using in-memory storage and parallel execution, providing significant speed and scale improvements. Future Grid integrated Hazelcast IMDG with Apache Cassandra, a distributed database for managing large amounts of structured data. This combination allowed Future Grid to overcome the limitations of traditional databases, providing continuous availability, linear scale performance, and operational simplicity. The integration enabled Future Grid to deliver real-time data processing and visualization, solving many challenges faced by the energy sector.

Impact #1	Future Grid's platform increased data transaction rates significantly using minimal hardware.
Impact #2	The platform provided real-time time series visualization, allowing engineering teams to act on data promptly.
Impact #3	The integration of Hazelcast IMDG and Apache Cassandra maintained high availability and horizontal scalability while delivering performance 1000x faster than disk-based approaches.

Impact #1

Future Grid's platform increased data transaction rates significantly using minimal hardware.

Impact #2

The platform provided real-time time series visualization, allowing engineering teams to act on data promptly.

Impact #3

The integration of Hazelcast IMDG and Apache Cassandra maintained high availability and horizontal scalability while delivering performance 1000x faster than disk-based approaches.

Benefit #1	Future Grid's solution was 1200% faster in a three-month pilot, reducing insight output time from 2 hours to 10 minutes.
Benefit #2	The platform reduced infrastructure costs by $10 million, using only $40,000 of hardware compared to the $10 million required for traditional relational databases.

Benefit #1

Future Grid's solution was 1200% faster in a three-month pilot, reducing insight output time from 2 hours to 10 minutes.

Benefit #2

The platform reduced infrastructure costs by $10 million, using only $40,000 of hardware compared to the $10 million required for traditional relational databases.

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Overview

Designing a real-time data platform for the “Internet of Energy”

Operational Impact

Quantitative Benefit